Molecular ecology meets systematic conservation planning
Authors: Erica S. Nielsen, Jeffrey O. Hanson, Silvia B. Carvalho, Maria Beger, Romina Henriques, Francine Kershaw, Sophie von der Heyden
Journal: Trends in Ecology & Evolution (2022)

Molecular ecology (ME) and systematic conservation planning (SCP) have seen rapid technological advancements in recent decades, but the use of molecular data to conserve different aspects of biodiversity remains operationally illusive.Here, the core principles of ME and SCP are combined in a novel way, showcasing how themes and corresponding data types from each field can complement each other.A framework, including five practical steps, is provided to guide both molecular ecologists and conservation planners to build systematic conservation plans that effectively integrate evolutionary features. Integrative and proactive conservation approaches are critical to the long-term persistence of biodiversity. Molecular data can provide important information on evolutionary processes necessary for conserving multiple levels of biodiversity (genes, populations, species, and ecosystems). However, molecular data are rarely used to guide spatial conservation decision-making. Here, we bridge the fields of molecular ecology (ME) and systematic conservation planning (SCP) (the 'why') to build a foundation for the inclusion of molecular data into spatial conservation planning tools (the 'how'), and provide a practical guide for implementing this integrative approach for both conservation planners and molecular ecologists. The proposed framework enhances interdisciplinary capacity, which is crucial to achieving the ambitious global conservation goals envisioned for the next decade. Integrative and proactive conservation approaches are critical to the long-term persistence of biodiversity. Molecular data can provide important information on evolutionary processes necessary for conserving multiple levels of biodiversity (genes, populations, species, and ecosystems). However, molecular data are rarely used to guide spatial conservation decision-making. Here, we bridge the fields of molecular ecology (ME) and systematic conservation planning (SCP) (the 'why') to build a foundation for the inclusion of molecular data into spatial conservation planning tools (the 'how'), and provide a practical guide for implementing this integrative approach for both conservation planners and molecular ecologists. The proposed framework enhances interdisciplinary capacity, which is crucial to achieving the ambitious global conservation goals envisioned for the next decade. The world is facing an anthropogenically driven biodiversity crisis [1.Díaz S.M. et al.The Global Assessment Report on Biodiversity and Ecosystem Services: Summary for Policy Makers. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2019Google Scholar,2.Sippel S. et al.Climate change now detectable from any single day of weather at global scale.Nat. Clim. Chang. 2020; 10: 35-41Crossref Scopus (134) Google Scholar] which must be urgently addressed to restore ecosystem functions, mitigate climate change, and maintain human well-being [3.Arneth A. et al.Post-2020 biodiversity targets need to embrace climate change.Proc. Natl. Acad. Sci. U. S. A. 2020; 117: 30882-30891Crossref PubMed Scopus (115) Google Scholar,4.Leclère D. et al.Bending the curve of terrestrial biodiversity needs an integrated strategy.Nature. 2020; 585: 551-556Crossref PubMed Scopus (343) Google Scholar]. Protected area establishment is a cornerstone for biodiversity conservation. The global protected area system coverage has been increasing, both in terrestrial and marine ecosystems [5.Bingham H.C. et al.Sixty years of tracking conservation progress using the World Database on Protected Areas.Nat. Ecol. Evol. 2019; 3: 737-743Crossref PubMed Scopus (56) Google Scholar]. However, this system is incomplete and falls short in safeguarding all levels of biodiversity [genes, populations (see Glossary), species, and ecosystems] [6.Visconti P. et al.Protected area targets post-2020.Science. 2021; 364: 239-241Crossref Scopus (227) Google Scholar]. Consequently, conservation actions are needed in places that will promote biodiversity resilience, adaptive potential, and species persistence in a changing world [7.Des Roches S. et al.Conserving intraspecific variation for nature's contributions to people.Nat. Ecol. Evol. 2021; 5: 574-582Crossref PubMed Scopus (66) Google Scholar]. Over the past 30 years, ecologists and evolutionary scientists have rallied to inform conservation decision-making. Molecular ecology (ME [8.Avise J.C. et al.Intraspecific phylogeography: the mitochondrial DNA bridge between population genetics and systematics.Annu. Rev. Ecol. Syst. 1987; 18: 489-522Crossref Google Scholar,9.Luikart G. et al.The power and promise of population genomics: from genotyping to genome typing.Nat. Rev. Genet. 2003; 4: 981-994Crossref PubMed Scopus (981) Google Scholar]) developed as a field for describing evolutionary processes in non-model/wild species and their populations within an ecological context. Meanwhile, systematic conservation planning (SCP [10.Margules C.R. Pressey R.L. Systematic conservation planning.Nature. 2000; 405: 243-253Crossref PubMed Scopus (4207) Google Scholar,11.Kirkpatrick J.B. An iterative method for establishing priorities for the selection of nature reserves: an example from Tasmania.Biol. Cons. 1983; 25: 127-134Crossref Scopus (379) Google Scholar]) emerged and provided a transparent, reproducible, and quantitative approach to identifying cost-effective priority areas for conservation. As these fields continue to develop, there is growing acceptance that they should be integrated to inform conservation decisions relevant to evolutionary objectives [12.Hoban S. et al.Global commitments to conserving and monitoring genetic diversity are now necessary and feasible.BioScience. 2021; 71: 964-976Crossref PubMed Scopus (67) Google Scholar]. Several ME subdisciplines can provide suitable data for SCP, such as extinction probabilities from conservation genetics or landscape connectivity from landscape genetics [13.Zizka A. et al.Existing approaches and future directions to link macroecology, macroevolution and conservation prioritization.Ecography. 2021; 6e05557Google Scholar]. Recent work has described ME data types most relevant to SCP, highlighting genetic diversity, dispersal, and effective population sizes (Ne) [14.Andrello M. et al.Evolving spatial conservation prioritization with intraspecific genetic data.Trends Ecol. Evol. 2022; 37: 553-564Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar]. However, little guidance exists to enable conservation practitioners and molecular ecologists to navigate when this evolutionary information is needed for conservation management, to implement various genetic/genomic data into conservation planning software, and to assess how well evolutionary objectives are being met [15.Holderegger R. et al.Conservation genetics: linking science with practice.Mol. Ecol. 2019; 28: 3848-3856Crossref PubMed Scopus (68) Google Scholar]. Here, we present a novel, integrative framework that reconciles ME and SCP to identify priority areas that will promote the long-term persistence of biodiversity. Our framework ensures that priority areas will be both ecologically and evolutionary driven, and meet criteria for real-world implementation. To achieve this, we bridge concepts, themes, and data used in ME and SCP to build a shared understanding of objectives needed to guide conservation decision-making (the 'why' section). We then outline procedures for: (i) defining conservation objectives; and using objectives to (ii) guide ME data collection; (iii) calculate molecular metrics; (iii) spatially interpolate metrics; and (iv) integrate them into SCP tools (the 'how' section). ME is grounded in understanding spatiotemporal genetic variation of natural populations, and offers multiple evolutionary metrics fundamental to assessing, and conserving, genetic diversity [16.Hoban S. et al.Global genetic diversity status and trends: towards a suite of Essential Biodiversity Variables (EBVs) for genetic composition.Biol. Rev. 2022; 97: 1511-1538Crossref PubMed Scopus (50) Google Scholar]. Major evolutionary themes have a strong focus on connectivity, adaptive potential, diversity, differentiation, and demography (hereafter, 'CADDD'). Connectivity describes the level of gene flow between populations across their distributional range, including source/sink dynamics; adaptive potential refers to the genetic variance potentially derived from local selective pressures and can pertain to the genomic vulnerability of a species or its resilience to environmental changes (here we restrict this definition to relate to the amounts/frequencies of genes assumed to have adaptive functions); diversity represents all extant genetic variation in a population or species (putatively neutral or adaptive); differentiation refers to the relative degree of the isolation as measured by differences in allele frequencies among individuals or genetic clusters (populations); and demography includes inbreeding levels and Ne, as well as historical changes in both. Thus, CADDD encompasses data across putatively neutral and adaptive loci fundamental for assuring the long-term persistence of intraspecific biodiversity [12.Hoban S. et al.Global commitments to conserving and monitoring genetic diversity are now necessary and feasible.BioScience. 2021; 71: 964-976Crossref PubMed Scopus (67) Google Scholar,17.von der Heyden S. Making evolutionary history count: biodiversity planning for coral reef fishes and the conservation of evolutionary processes.Coral Reefs. 2017; 36: 183-194Crossref Scopus (23) Google Scholar,18.Clark R.D. et al.Genomic signatures of spatially divergent selection at clownfish range margins.Proc. R. Soc. B Biol. Sci. 2021; 288: 20210407Crossref PubMed Scopus (3) Google Scholar]. We propose that understanding how metrics of CADDD integrate into the defining themes of SCP can offer more clarity about whether molecular metrics are needed for conservation objectives. SCP is a transparent, reproducible, and quantitative approach concerned with the optimal application of spatially explicit conservation management actions to promote the representation and persistence of biodiversity [10.Margules C.R. Pressey R.L. Systematic conservation planning.Nature. 2000; 405: 243-253Crossref PubMed Scopus (4207) Google Scholar,19.Watson J.E.M. et al.Systematic conservation planning: past, present and future.Conserv. Biogeogr. 2011; 1: 136-160Crossref Scopus (90) Google Scholar]. It involves generating prioritizations of sites to achieve conservation objectives, while accounting for social, economic, and political constraints efficiently. Conservation objective examples include safeguarding habitat of threatened species, restoring connectivity between isolated populations, or securing a comprehensive range of ecosystems [10.Margules C.R. Pressey R.L. Systematic conservation planning.Nature. 2000; 405: 243-253Crossref PubMed Scopus (4207) Google Scholar]. Achieving such objectives requires clear and quantitative choices of the evolutionary levels (genes, populations, species, and ecosystems) and facets (taxonomic, phylogenetic, and functional) of biodiversity that are to be included [20.Carvalho S.B. et al.Spatial conservation prioritization of biodiversity spanning the evolutionary continuum.Nat. Ecol. Evol. 2017; 1: 0151Crossref PubMed Scopus (64) Google Scholar,21.Pollock L.J. et al.Protecting biodiversity (in all its complexity): new models and methods.Trends Ecol. Evol. 2020; 35: 1119-1128Abstract Full Text Full Text PDF PubMed Scopus (79) Google Scholar]. SCP involves dividing the planning region into planning units, calculating the amount of each conservation feature in each one, and identifying sets of planning units that meet conservation goals. To ensure that prioritizations are feasible for implementation, SCP accounts for economic and opportunity costs, and resource-use requirements (e.g., ensuring sufficient areas for agricultural production or recreational fishing [22.Moilanen A. et al.Spatial Conservation Prioritization: Quantitative Methods and Computational Tools. Oxford University Press, 2009Crossref Google Scholar]). To maximize the efficiency of SCP, different algorithms and tools have been developed, including Marxan [23.Ball I.R. et al.Marxan and relatives: software for spatial conservation prioritisation.in: Moilanen A. Spatial Conservation Prioritisation: Quantitative Methods and Computational Tools. Oxford University Press, 2009: 185-195Google Scholar], Zonation [24.Moilanen A. et al.Zonation—Spatial Conservation Planning Methods and Software. Version 4. User Manual. C-BIG Conservation Biology Informatics Group, 2014Google Scholar], and the prioritizr R package [25.Hanson J.O. et al.Prioritizr: Systematic Conservation Prioritization in R. R Package Version 4.1. 5. R Project, 2020Google Scholar] (Box 1). SCP has a set of principles to guide effective decision-making, which can be easily integrated with CADDD (Figure 1 and Table 1). Comprehensiveness refers to a system of conservation areas that sample the main components of biodiversity within a region of interest (e.g., ecoregions, habitat types, or species). Molecular data can provide insights into comprehensiveness at the species level using phylogenetic diversity as a conservation feature [26.Owen N.R. et al.Global conservation of phylogenetic diversity captures more than just functional diversity.Nat. Commun. 2019; 10: 859Crossref PubMed Scopus (63) Google Scholar] or to find areas that best represent species, lineages, and populations [27.Rosauer D.F. et al.Real-world conservation planning for evolutionary diversity in the Kimberley, Australia, sidesteps uncertain taxonomy.Conserv. Lett. 2018; 11e12438Crossref Scopus (32) Google Scholar]. Adequacy refers to the ability of a protected area network to promote long-term persistence of biodiversity and to ensure the viability of populations, species, and habitats. Molecular data can provide important insights into adequacy, by identifying evolutionary significant units (ESUs), measuring differentiation, quantifying genetic diversity and adaptive potential, and estimating Ne [28.Corlett R.T. A bigger toolbox: biotechnology in biodiversity conservation.Trends Biotechnol. 2017; 35: 55-65Abstract Full Text Full Text PDF PubMed Scopus (91) Google Scholar]. Representativeness refers to the need to capture the full variety of biodiversity within conservation areas, ideally within each level of biological organization. Molecular data are imperative to assure that the diversity within each species is suitably represented, for instance, by prioritizing areas to capture each ESU or private alleles [29.Nielsen E.S. et al.Multispecies genetic objectives in spatial conservation planning.Conserv. Biol. 2017; 31: 872-882Crossref PubMed Scopus (38) Google Scholar,30.Paz-Vinas I. et al.Systematic conservation planning for intraspecific genetic diversity.Proc. R. Soc. B Biol. Sci. 2018; 285: 20172746Crossref PubMed Scopus (52) Google Scholar]. We distinguish interspecific data as applying to comprehensiveness, and intraspecific measures (e.g., number of populations) as applying to representativeness. Efficiency highlights the need to consider the economic and opportunity costs of foregone commercial and recreational activities [31.Watson K.B. et al.Effects of human demand on conservation planning for biodiversity and ecosystem services.Conserv. Biol. 2019; 33: 942-952Crossref PubMed Scopus (56) Google Scholar]. While molecular data are unlikely to contribute directly to reserve efficiency, it can facilitate it by providing cost-effective data to guide SCP procedures. Connectivity relates to conservation plans that counteract landscape fragmentation and protect ecological corridors that underpin biodiversity persistence and ecosystem functioning [32.Keeley A.T.H. et al.Thirty years of connectivity conservation planning: an assessment of factors influencing plan implementation.Environ. Res. Lett. 2019; 14103001Crossref Scopus (60) Google Scholar]. Molecular connectivity can inform the objective of establishing connected reserve areas, within which the flow of genes can lead to demographic and genetic benefits in metapopulations [33.Keeley A.T.H. et al.Connectivity metrics for conservation planning and monitoring.Biol. Conserv. 2021; 255109008Crossref Scopus (52) Google Scholar].Box 1Conservation planning decision support toolsCurrently, the most commonly used decision support tools for conservation planning are Marxan, Zonation, and the prioritizr R package [23.Ball I.R. et al.Marxan and relatives: software for spatial conservation prioritisation.in: Moilanen A. Spatial Conservation Prioritisation: Quantitative Methods and Computational Tools. Oxford University Press, 2009: 185-195Google Scholar, 24.Moilanen A. et al.Zonation—Spatial Conservation Planning Methods and Software. Version 4. User Manual. C-BIG Conservation Biology Informatics Group, 2014Google Scholar, 25.Hanson J.O. et al.Prioritizr: Systematic Conservation Prioritization in R. R Package Version 4.1. 5. R Project, 2020Google Scholar]. These tools frame conservation planning as mathematical optimization problems and solve them to generate prioritizations. Both prioritizr and Zonation can import spatially explicit data formats (e.g., ESRI Shapefile or GeoTIFF). Additional software, such as CLUZ and QMarxan [88.Smith R. The CLUZ plugin for QGIS: designing conservation area systems and other ecological networks.RIO. 2019; 5e33510Crossref Google Scholar]iii, are available to assist with spatial processing for Marxan (step 4 of 'The how' section). In addition, Marxan Connect [70.Daigle R.M. et al.Operationalizing ecological connectivity in spatial conservation planning with Marxan Connect.Methods Ecol. Evol. 2020; 11: 570-579Crossref Scopus (55) Google Scholar] is a general-purpose tool that enables inputting measures of connectivity, such as demographic (i.e., animal tracking/dispersal data), genetic, or structural connectivity (i.e., landscape resistance) as conservation features in Marxan prioritizations.Each of the tools uses different mathematical formulations to express optimization problems. For example, Marxan uses the minimum set formulation wherein the goal is to identify the 'cheapest' set of planning units for implementing management actions, while ensuring that the expected amount of each feature meets a minimum threshold (termed 'representation target'). Zonation uses a budget-limited formulation in which the goal is to maximize the overall amount of each feature, while ensuring that the total cost of implementing management actions does not exceed a budget threshold. Finally, prioritizr allows users to create custom-built problem formulations (including both minimum set and budget-limited formulations). Additionally, these tools, except for Marxan (but see Marxan with Zones [89.Watts M.E. et al.Marxan with Zones: software for optimal conservation based land- and sea-use zoning.Environ. Model. Softw. 2009; 24: 1513-1521Crossref Scopus (421) Google Scholar]), can accommodate multiple management actions and zones.These tools also use different algorithms to generate prioritizations. Zonation uses a backward heuristic algorithm to iteratively rank planning units [24.Moilanen A. et al.Zonation—Spatial Conservation Planning Methods and Software. Version 4. User Manual. C-BIG Conservation Biology Informatics Group, 2014Google Scholar]. As such, thresholds are often imposed to identify planning units for prioritization (e.g., top 20% ranked planning units). Marxan uses the simulated annealing algorithm, which can identify prioritizations that are more cost-effective compared with heuristic algorithms [23.Ball I.R. et al.Marxan and relatives: software for spatial conservation prioritisation.in: Moilanen A. Spatial Conservation Prioritisation: Quantitative Methods and Computational Tools. Oxford University Press, 2009: 185-195Google Scholar]. Although both of these algorithms could identify cost-effective prioritizations, they do not guarantee optimality [90.Hanson J.O. et al.Optimality in prioritizing conservation projects.Methods Ecol. Evol. 2019; 10: 1655-1663Crossref Scopus (15) Google Scholar]. Non-optimal solutions can misdirect conservation resources and lead to inefficiencies. To overcome this limitation, prioritizr uses exact algorithms, which guarantee optimality [25.Hanson J.O. et al.Prioritizr: Systematic Conservation Prioritization in R. R Package Version 4.1. 5. R Project, 2020Google Scholar], while Marxan and Zonation offer flexibility to quickly generate multiple solutions for stakeholder negotiations [23.Ball I.R. et al.Marxan and relatives: software for spatial conservation prioritisation.in: Moilanen A. Spatial Conservation Prioritisation: Quantitative Methods and Computational Tools. Oxford University Press, 2009: 185-195Google Scholar,24.Moilanen A. et al.Zonation—Spatial Conservation Planning Methods and Software. Version 4. User Manual. C-BIG Conservation Biology Informatics Group, 2014Google Scholar].Table 1Matching objectives and metrics across fieldsaThis table highlights the specific ways in which different components of conservation and molecular fields intersect, including examples of how different data types can be used to meet the respective objectives for protected area (PA) systems.Conservation principlesMolecular principlesConnectivity (gene flow)Adaptive capacityDiversityDifferentiationDemographyComprehensive : PA system should contain broad range of biotic elements (e.g., species, ecoregions, etc.)(a)(b)(c)OBJECTIVE: PA network to include all species present in systemMETRICS:Phylogenetic endemismPhylogenetic diversity(d)OBJECTIVE: PA network to include all species present in systemMETRICS:Phylogenetic trees based on genetic differencesPhylogeographic mapsPhylogenetic distinctiveness(e)Adequate: PA system should contain a sufficient sample of each biotic element (e.g., amount of habitat) to achieve its long-term persistence(f)(g)OBJECTIVE: PA system to include a sufficient sample of a range of adaptive diversity to maximize probability of persistence under different disturbancesMETRICS:Genomic vulnerabilityOutlier allele frequencyRare/private allelesPopulation adaptive index (PAI)Adaptive score (Sadapt)(h)OBJECTIVE: PA system to include areas that contain genetically diverse populations to ensure long-term persistence (e.g., low inbreeding, high heterozygosity) METRICS:Proportion of genetic 'unit' (species, population, deme) required to maximize genetic diversityNucleotide and haplotype diversityHeterozygosityAllelic richness(i)OBJECTIVE: PA system to include unique populationsMETRICS:Population structure (admixture plots)FST-based metricsGenetic distances (i.e., Nei's genetic distance)(j)OBJECTIVE: PA systems that maintain adequate Ne (e.g., prioritizing management of populations with high Ne, or past demographic stability)METRICS:NeInbreeding coefficientsDemographic history simulationsRepresentative: PA system should capture a sample of each biotic element that reflects diversity within that element (e.g., different ecotypes, etc.)(k)(l)OBJECTIVE: PA system to include a sufficient sample of a range of adaptive diversity and selective genotypesMETRICS:Allele frequencies of candidate loci pertaining to different environmental featuresAdaptive genetic diversityDifferently adapted lineages(m)OBJECTIVE: PA system to include a sufficient sample of a range of neutral diversity, including rare allelesMETRICS:Haplotype networksRare/private allelesNeutral genetic clustering(n)OBJECTIVE: Represent evolutionarily unique populations/lineagesMETRICS:Population structure (admixture plots/PCAs)FST-based metrics Genetic distancesLineage delineations(o)OBJECTIVE: PA system to include a range of populations dynamics (e.g., to maintain metapopulation processes, such as adaptation, gene flow and divergence)METRICS:Measures of hybridization and inbreedingNe and population growth ratesSimulated eco-evolutionary range dynamicsEfficient: PA system should be cost-effectiveConnectivity: PA system should capture linkages that underpin persistence among sites(p)OBJECTIVE: Maintain and/or enhance connectivity between populations METRICS:Migration rates (m)Effective number of migrantsSlatkin's psi(q)OBJECTIVE: Ensure that populations with potentially beneficial adaptations that export individuals are represented by a PA system (e.g., genetic rescue)METRICS:Allele frequencies of candidate loci or rare/private alleles(r)OBJECTIVE: Ensure connectivity between places with high diversity is prioritized over othersMETRICS:Connectivity measures in combination with:Nucleotide or haplotype diversityHeterozygosityAllelic richness(s)(t)OBJECTIVE: Maintain and/or enhance connectivity between source and sink populations (i.e., ensure effective number of migrants between populations)METRICS:Effective number of migrantsConnectivity and Ne measuresMigration and resistance surfacesa This table highlights the specific ways in which different components of conservation and molecular fields intersect, including examples of how different data types can be used to meet the respective objectives for protected area (PA) systems. Open table in a new tab Currently, the most commonly used decision support tools for conservation planning are Marxan, Zonation, and the prioritizr R package [23.Ball I.R. et al.Marxan and relatives: software for spatial conservation prioritisation.in: Moilanen A. Spatial Conservation Prioritisation: Quantitative Methods and Computational Tools. Oxford University Press, 2009: 185-195Google Scholar, 24.Moilanen A. et al.Zonation—Spatial Conservation Planning Methods and Software. Version 4. User Manual. C-BIG Conservation Biology Informatics Group, 2014Google Scholar, 25.Hanson J.O. et al.Prioritizr: Systematic Conservation Prioritization in R. R Package Version 4.1. 5. R Project, 2020Google Scholar]. These tools frame conservation planning as mathematical optimization problems and solve them to generate prioritizations. Both prioritizr and Zonation can import spatially explicit data formats (e.g., ESRI Shapefile or GeoTIFF). Additional software, such as CLUZ and QMarxan [88.Smith R. The CLUZ plugin for QGIS: designing conservation area systems and other ecological networks.RIO. 2019; 5e33510Crossref Google Scholar]iii, are available to assist with spatial processing for Marxan (step 4 of 'The how' section). In addition, Marxan Connect [70.Daigle R.M. et al.Operationalizing ecological connectivity in spatial conservation planning with Marxan Connect.Methods Ecol. Evol. 2020; 11: 570-579Crossref Scopus (55) Google Scholar] is a general-purpose tool that enables inputting measures of connectivity, such as demographic (i.e., animal tracking/dispersal data), genetic, or structural connectivity (i.e., landscape resistance) as conservation features in Marxan prioritizations. Each of the tools uses different mathematical formulations to express optimization problems. For example, Marxan uses the minimum set formulation wherein the goal is to identify the 'cheapest' set of planning units for implementing management actions, while ensuring that the expected amount of each feature meets a minimum threshold (termed 'representation target'). Zonation uses a budget-limited formulation in which the goal is to maximize the overall amount of each feature, while ensuring that the total cost of implementing management actions does not exceed a budget threshold. Finally, prioritizr allows users to create custom-built problem formulations (including both minimum set and budget-limited formulations). Additionally, these tools, except for Marxan (but see Marxan with Zones [89.Watts M.E. et al.Marxan with Zones: software for optimal conservation based land- and sea-use zoning.Environ. Model. Softw. 2009; 24: 1513-1521Crossref Scopus (421) Google Scholar]), can accommodate multiple management actions and zones. These tools also use different algorithms to generate prioritizations. Zonation uses a backward heuristic algorithm to iteratively rank planning units [24.Moilanen A. et al.Zonation—Spatial Conservation Planning Methods and Software. Version 4. User Manual. C-BIG Conservation Biology Informatics Group, 2014Google Scholar]. As such, thresholds are often imposed to identify planning units for prioritization (e.g., top 20% ranked planning units). Marxan uses the simulated annealing algorithm, which can identify prioritizations that are more cost-effective compared with heuristic algorithms [23.Ball I.R. et al.Marxan and relatives: software for spatial conservation prioritisation.in: Moilanen A. Spatial Conservation Prioritisation: Quantitative Methods and Computational Tools. Oxford University Press, 2009: 185-195Google Scholar]. Although both of these algorithms could identify cost-effective prioritizations, they do not guarantee optimality [90.Hanson J.O. et al.Optimality in prioritizing conservation projects.Methods Ecol. Evol. 2019; 10: 1655-1663Crossref Scopus (15) Google Scholar]. Non-optimal solutions can misdirect conservation resources and lead to inefficiencies. To overcome this limitation, prioritizr uses exact algorithms, which guarantee optimality [25.Hanson J.O. et al.Prioritizr: Systematic Conservation Prioritization in R. R Package Version 4.1. 5. R Project, 2020Google Scholar], while Marxan and Zonation offer flexibility to quickly generate multiple solutions for stakeholder negotiations [23.Ball I.R. et al.Marxan and relatives: software for spatial conservation prioritisation.in: Moilanen A. Spatial Conservation Prioritisation: Quantitative Methods and Computational Tools. Oxford University Press,